Landsat ARD Coverage Extent
This week’s lab will feature exploration of the following 3 themes:
During this week’s Lecture Demo Lab will utilize the following data:
What is a spectral index?
A spectral index is a mathematical equation that is applied on the various spectral bands of an image per pixel.
The most common mathematical formulas that are used is the normalized difference:
(Bx - By)/(Bx + By)
In practical terms, this is the difference between two selected bands normalized by their sum. This type of calculus is very useful to minimize (as much as possible) the effects of illumination (shadows in mountainous regions, cloud shadows, etc) and enhance spectral features that are not visible initially. In other words, the calculation ‘normalizes’ the image, resulting in scaled values and a easily understood color ramp.
To Start, the input data for the lecture demo will be the same as that of the assignment itself, accessed through Earth Explorer:
Landsat C2 US ARD
Horizontal/Vertical Position ID of AOI ARD Tile
This Tile is cloud-free, full coverage over the AOI, and is within the growing season - 6/20/21
Surface Reflectance Bundle
Surface Temperature Bundle
SR_springfield_composite.tif located in the Part I directory. We can set the band combination to 5/4/3 to see the vegetative cover in/around the city relative to impervious surfaces:Preview Imagery in Composite 5/4/3
NDVI = (NIR Band - Red Band / (NIR Band + Red Band)
NDVI = (Band 5 – Band 4) / (Band 5 + Band 4)
Utilize Either the Composite Bands 4 and 5, or Import Them Separately
SR_springfield_composite.tif:("LC08_CU_018011_20210620_20210703_02_SR_B5@1"-"LC08_CU_018011_20210620_20210703_02_SR_B4@1")/("LC08_CU_018011_20210620_20210703_02_SR_B5@1"+"LC08_CU_018011_20210620_20210703_02_SR_B4@1")
NDVI low > high Symbolization
NDVI Values Interpretation
NDBI = (SWIR - NIR) / (SWIR + NIR)
NDBI = (Band 6 – Band 5) / (Band 6 + Band 5)
SR_springfield_composite.tif:("LC08_CU_018011_20210620_20210703_02_SR_B6@1" - "LC08_CU_018011_20210620_20210703_02_SR_B5@1") / ("LC08_CU_018011_20210620_20210703_02_SR_B6@1" + "LC08_CU_018011_20210620_20210703_02_SR_B5@1")
NBDI vs NDVI
Working with remote sensed imagery in a desktop environment typically involves much pre-processing work devoted to conversions and corrections. This paper does an excellent job of summarizing these pre-processing tasks for Landsat imagery:
One of the more onerous analysis tasks is the pre-processing of imagery for Land Surface Temperature - a topic that is trending for all the wrong reasons due to climate change coupled with social, demographic and built environmental inequities, especially in large cities.
Utilizing the ARD dataset for Landsat imagery, a good portion of pre-processing work has been completed by Landsat/USGS.
Currently in tar.gz compression, the directory is unzipped, and the ST raster is isolated. This raster is imported into QGIS via the Data Source Manager as a typical raster product:
The Surface Temperature ST Band
Surface Temperature (ST) – Represents the temperature of the Earth’s surface in Kelvin (K).
Kelvin Values
Note the Kelvin Values 28804 - 49411
Here the digital numbers need to be scaled accordingly. If the value is to be expressed in Celcius or Fahrenheit, a further conversion needs to take place via the raster calculator. This will be accomplished in two steps:
Scale the raster values in raster calculator
Apply the following temperature conversion where K is the scaled raster values and F is the Fahrenheit temperature measurement:
F = 1.8(K - 273) + 32
To Start, open the Raster Calculator and apply the following scale factor and offset value as noted in the product metadata, exporting the product to the data folder as st_scaled:
"LC08_CU_018011_20210620_20210703_02_ST_B10@1" * 0.00341802 + 149Scaled + Offset Kelvin Values
Next, apply the temperature conversion based on the formula for kelvin to fahrenheit units, and output as st_fahrenheit
1.8 * ("st_scaled@1" - 273) + 32Review the resulting raster values, Kelvin vs Fahrenheit:
Kelvin vs Fahrenheit
ST values relative to Landcover
Part III Data Structure
Part III Input Data Loaded to QGIS Layers Panel
Its important to note at this juncture that there are two primary reasons for surface interpolation. First, like the current mapping, a pattern to the data beyond the feature input is sought. Second, and most related to climatology datasets used this week in the assignment, is the intent to ‘fill’ in missing data and ‘smooth’ data across geographic expanse. This is the situation where values between location A and location B need to be ‘filled’ in. In effect, surface interpolation gives up the specificity of input location precision to get back better predictive values across spaces - including those ‘between’ known data input locations and their values.
Prior to running a density surface, a defined search radius needs to be determined. This is the euclidean distance from a individual cell in the resulting surface outwards to defined a ‘search distance’. This distance can be derived by a formula or it can be ‘eyeballed’. Based on a formula calculation, the optimal distance for the dataset was found to be 582 feet.
To learn more about how to derive a optimal distance - known as hopt - the following video outlines steps to do so.
To Start the process, there are two build-in tools and also a plug-in tool. Here we will use the Heatmap (Kernal Density Estimation) tool:
Heatmap (Kernal Density Estimation) Tool
Input Parameters
Input is the points feature MVC_11_8_2019
Radius = 582
Pixel Size X and Y = 100 (note: the feature is projected as NYSP, units Feet; thus all measurments are in square feet or linear feet, depending on the measurement type.
Output = Raw - this results in a estimated count per pixel of accidents
Kernel Shape = Quartic - default
There will be no weights; that is, just the locations of the accidents, not their variables, will be estimated.
View result:
Interpolation Result
Symbolization
In Review of the resulting surface, apply 50% transparency and drape the kernel raster over a satellite base map. Here the ESRI Satellite base is utilized:
Review - Small Scale
In Review of the various hotspots throughout the kernel density result, its now clear that density of accidents is spatially consistent with bridges and tunnels throughout Manhattan. Without the density surface, this concentration pattern is largely missing from the input points feature itself. Thus we get a quantitative surface with cell values of accidents and and insightful visualization:
Lower Manhattan:
Review - Large Scale
Review - Large Scale
Review - Large Scale